# Abstract Regulations in many jurisdictions require Certified Public Accountants (CPAs) to attest to each audit report they certify, typically by affixing a signature or seal. However, the digitization of financial reporting makes it straightforward to reuse a stored signature image across multiple reports---whether by administrative stamping or firm-level electronic signing systems---potentially undermining the intent of individualized attestation. Unlike signature forgery, where an impostor imitates another person's handwriting, *non-hand-signed* reproduction involves the legitimate signer's own stored signature image being reproduced on each report, a practice that is visually invisible to report users and infeasible to audit at scale through manual inspection. We present an end-to-end AI pipeline that automatically detects non-hand-signed auditor signatures in financial audit reports. The pipeline integrates a Vision-Language Model for signature page identification, YOLOv11 for signature region detection, and ResNet-50 for deep feature extraction, followed by a dual-descriptor verification combining cosine similarity of deep embeddings with difference hashing (dHash). For threshold determination we apply three statistically independent methods---Kernel Density antimode with a Hartigan dip test, Burgstahler-Dichev/McCrary discontinuity, and EM-fitted Beta mixtures with a logit-Gaussian robustness check---at both the signature level and the accountant level. Applied to 90,282 audit reports filed in Taiwan over 2013--2023 (182,328 signatures from 758 CPAs) the three methods reveal an informative asymmetry: signature-level similarity forms a continuous quality spectrum with no clean two-mechanism bimodality, while accountant-level aggregates are cleanly trimodal (BIC-best $K = 3$), reflecting that individual signing *behavior* is close to discrete even when pixel-level output *quality* is continuous. The accountant-level 2-component crossings yield principled thresholds (cosine $= 0.945$, dHash $= 8.10$). A major Big-4 firm is used as a *replication-dominated* (not pure) calibration anchor, with interview and visual evidence supporting majority non-hand-signing and a minority of hand-signers. Validation against 310 pixel-identical signature pairs and a low-similarity negative anchor yields perfect recall at all candidate thresholds. To our knowledge, this represents the largest-scale forensic analysis of auditor signature authenticity reported in the literature.